人間との相互作用をデータから特徴づける電動義足のモデルベース制御
Data-driven Characterization of Human Interaction for Model-based Control of Powered Prostheses
モーションキャプチャデータから歩行軌道と人間-義足間の相互作用力を抽出し、そのばらつきを力のチューブとしてモデル化した上で、ロバストCLF-QP制御により追加センサなしで安定歩行を実現する手法を提案した。
著者: Rachel Gehlhar, Yuxiao Chen, Aaron D. Ames
分類: cs.RO
原文アブストラクト
This paper proposes a data-driven method for powered prosthesis control that achieves stable walking without the need for additional sensors on the human. The key idea is to extract the nominal gait and the human interaction information from motion capture data, and reconstruct the walking behavior with a dynamic model of the human-prosthesis system. The walking behavior of a human wearing a powered prosthesis is obtained through motion capture, which yields the limb and joint trajectories. Then a nominal trajectory is obtained by solving a gait optimization problem designed to reconstruct the walking behavior observed by motion capture. Moreover, the interaction force profiles between the human and the prosthesis are recovered by simulating the model following the recorded gaits, which are then used to construct a force tube that covers all the interaction force profiles. Finally, a robust Control Lyapunov Function (CLF) Quadratic Programming (QP) controller is designed to guarantee the convergence to the nominal trajectory under all possible interaction forces within the tube. Simulation results show this controller's improved tracking performance with a perturbed force profile compared to other control methods with less model information.
